AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential
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Abstract (arXiv)
Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we present AlphaNet, a local-frame-based equivariant model that simultaneously improves computational efficiency and predictive precision for interatomic interactions. By constructing equivariant local frames with learnable geometric transitions, AlphaNet encodes atomic environments with enhanced representational capacity, achieving state-of-the-art accuracy in energy and force predictions. Extensive benchmarks on large-scale datasets spanning molecular reactions, crystal stability, and surface catalysis (Matbench Discovery and OC2M) demonstrate its superior performance over existing neural network interatomic potentials while ensuring scalability across diverse system sizes with varying types of interatomic interactions. The synergy of accuracy, efficiency, and transferability positions AlphaNet as a transformative tool for modeling multiscale phenomena, decoding dynamics in catalysis and functional interfaces, with direct implications for accelerating the discovery of complex molecular systems and functional materials.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 60 references that are in the map. First posted on arXiv 13 Jan 2025.
Builds on
22 of 60 references.
References · 22
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- 2018Self-Attention with Relative Position Representationscited by 1,112
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- 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
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- 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
- 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
10 more
- 2023Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNscited by 113
- 2017DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicscited by 79
- 2021SE(3) Equivariant Graph Neural Networks with Complete Local Framescited by 75
- 2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
- 2024Orb: A Fast, Scalable Neural Network Potentialcited by 63
- 2022GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasetscited by 61
- 2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 46
- 2024The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domainscited by 40
- 2023Matbench Discovery -- A framework to evaluate machine learning crystal stability predictionscited by 34
- 2022DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulationcited by 15
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.